Contextual feature fusion and refinement network for camouflaged object detection

Camouflaged object detection (COD) is a challenging task due to its irregular shape and color similarity or even blending into the surrounding environment. It is difficult to achieve satisfactory results by directly using salient object detection methods due to the low contrast with the surrounding environment and obscure object boundary in camouflaged object detection. To determine the location of the camouflaged objects and achieve accurate segmentation, the interaction between features is essential. Similarly, an effective feature aggregation method is also very important. In this paper, we propose a contextual fusion and feature refinement network (CFNet). Specifically, we propose a multiple-receptive-fields-based feature extraction module (MFM) that obtains features from multiple scales of receptive fields. Then, the features are input to an attention-based information interaction module (AIM), which establishes the information flow between adjacent layers through an attention mechanism. Finally, the features are fused and optimized layer by layer using a feature fusion module (FFM). We validate the proposed CFNet as an effective COD model on four benchmark datasets, and the generalization ability of our proposed model is verified in the salient object detection task.